DEFECT PREDICTION USING CASE-BASED REASONING: AN ATTRIBUTE WEIGHTING TECHNIQUE BASED UPON SENSITIVITY ANALYSIS IN NEURAL NETWORKS

Abstract

Software defect prediction is an acknowledged approach used to achieve better product quality and to better utilize resources needed for that purpose. One known method for predicting the number of defects is to apply case-based reasoning (CBR). In this paper, different attribute weighting techniques for CBR-based defect prediction are analyzed. One of the weighting techniques used in this work, Sensitivity Analysis based on Neural Networks (SANN), is based on sensitivity analysis of the impact of attributes as part of neural network analysis. Neural networks are applicable when there are non-linear and complicated relationships among the attributes. Since weighting plays a key role in the CBR model, using an efficient weight calculation method can change the results. The results of SANN are compared with applying uniform weights and weights gained from Multiple Linear Regression (MLR).Evaluation of the accuracy of the overall method for applying the three different weighting techniques is done over five data sets, comprising about 5000 modules from NASA. Two quality measures are applied: Average Absolute Error (AAE) and Average Relative Error (ARE). In addition to the variation of weighting techniques, the impact of varying the number of nearest neighbors is studied.The three main results of the empirical analysis are: (i) In the majority of cases, SANN achieves the most accurate results; (ii) uniform weighting performs better than the MLR-based weighting heuristic; and (iii) there is no significant preference pattern for defining the number of similar objects used for prediction in CBR.